Why Establish Baseline Models | A Detailed Guide

How do you know if your advanced ML model performs well? How do we measure its predictive capabilities?

Even though you can use key metrics to assess model performance, it would be difficult to interpret these metrics without sufficient context. In short, iterating sophisticated ML models is challenging without proper insight into the model performance.

Baseline models serve as a guide to determine if the advanced model achieves its purpose. They set a performance benchmark to measure the efficiency of complex ML models.

This blog post explores baseline models in depth – what they are, their significance, how to build a baseline model, the challenges involved, evaluation metrics, and examples of real-world applications. 

What is a Baseline Model? 

A baseline model in machine learning is a basic model that predicts data outcomes. Baseline models are the starting point of analysis. They enable you to evaluate the performance of more complex models and the impact of different features. Overall, the main function of baseline models is to inspect and provide information on the results of trained models.

Carnegie Mellon University defines a baseline model as ‘a simple model that provides reasonable results on a task and does not require much expertise and time to build.’

According to Emmanuel Ameisen, Head of AI at Insight Data Science, in machine learning basics, the common baseline models include,

• linear regression for forecasting continuous values,
• logistic regression for classifying structured data,
• pre-trained convolutional neural networks for vision-related work and
• recurrent neural networks and gradient-boosted trees for sequence modeling

Why Establish a Baseline Model?

Baseline models provide the starting point for more advanced ML model evaluation. Here’s why you should build a baseline model.

1. Start the ML Development Process Faster

In ML development, the insights gained from one cycle inform the next. Additionally, the iteration speed of practitioners dictates how well they transition from prototype to product stage.

Baseline models are simple and fast to build. With data-centric AI platforms like MarkovML, it just takes a few clicks, without coding, to build baseline models. These models help quickly start ML development and provide deep insights into the process that can potentially save several resources in the long run.

2. Get Insights and Improve Decision-Making

According to a report by Carnegie Mellon University, ‘a baseline takes only 10% of the time to develop, but will get us 90% of the way to achieve reasonably good results.’

Baseline models serve as a source of information for decision-making. They help analyze model complexity in more advanced models and help with resource allocation, model selection, and model iteration. Baseline models are also great for boosting your data understanding, identifying bugs, and testing assumptions.

3. Compare Model Performance

Baseline models are great for model iteration. They help determine if model complexity translates into better model performance. Compared with the baseline, if a model does not outperform, it suggests issues with the data or approach. Thus, you can identify the issues and take steps toward the model’s iterative improvement.

Steps to Create a Baseline Model 

Building a baseline model involves several steps – from identifying the problem to data collection, evaluation, and model iteration. Follow these steps to create your baseline model successfully.

1. Define the Problem

Baseline models are benchmarks in ML applications. They interpret the results of trained models. So, before building the baseline model, work on your problem statement and then step into the data.

2. Gather and Preprocess Data

Collect the relevant data sets for your identified problem and clean it before analysis. Data preprocessing involves:

• Figuring out missing values
• Eliminating redundant data
• Encoding categorical variables
• Categorizing the data into training and testing sets

3. Choose the Baseline Model

Choose a simple algorithm for your baseline model, such as linear regression that works well for regression problems, logistic regression for binary classification, or decision trees for both. Naïve Bayes classifier is also a common algorithm for baseline models used for classification tasks.

4. Feature Selection

Effective baseline models are created on relevant features. Feature selection involves identifying attributes that contribute significantly to the model's performance, making it simpler and more interpretable. Proper feature engineering contributes to the baseline model’s accuracy.

5. Train and Test the Baseline Model

For this step, you’ll need the train and test datasets. Use the training dataset to train your chosen baseline model. Functions like fit ( ) are used for the training operation of your baseline model. Adjust the given parameters of the function as required and monitor the training process to ensure that it does not overfit.

Testing the baseline model involves comparing the testing datasets for accuracy scores. Testing the model gives an idea of how well the trained model performs in a validation set.

6. Establish Baseline Performance Metrics

Set up the metrics necessary for evaluating the baseline model, such as accuracy, precision, F1-score, and recall, to offer a complete view of the model performance.

7. Evaluate and Document the Baseline Model Performance

Based on the established metrics, assess the model’s performance and document the metrics achieved by the baseline model, the codes, and parameter choices. These metrics are references for assessing the effectiveness of more complex models.

8. Iterate and Improve

Once documented, start analyzing the baseline model’s performance results and iterate on your baseline model. Adjust the parameters, try out different codes, and algorithms, or incorporate more features in the baseline model to improvise. This will eventually improve the baseline model’s performance.

9. Communicate to Stakeholders

Share the documented performance metrics of your baseline model with your stakeholders to make informed decisions regarding the robustness of your baseline model.

MarkovML’s data-centric AI platform builds baseline models in a few clicks. All you need to do is-

• Upload/ Import your dataset
• Automatically generate a baseline model app on MarkovML
• Choose your app’s name and the period for which you want it to be active
• Use the model as a web application or a RESTful API

Baseline Model Evaluation Metrics 

There are several evaluation metrics for baseline models. Here are a few metrics for each of the categories.

1. Classification Problems

F1 score – It is the harmonic mean of recall and precision values for specific classification problems.

Gain and Lift charts – These charts check the rank ordering of the probabilities.

K-S or Kolmogorov-Smirnov chart – This chart measures the classification model’s performance. It is a measure of the degree of separation between negative and positive distribution.

2. Regression Problems

Root Mean Squared Error (RMSE) – This metric assumes that errors are unbiased and follow a normal distribution. This metric shows the probable magnitude of the error.

R-Squared – This metric shows how good is the regression model compared to a basic model that predicts the target mean value from the train set predictions.

3. Imbalanced Datasets

Classification accuracy – This metric provides a summary of the classification model performance. It is calculated by dividing correct predictions by total predictions.

Recall – This metric measures how often the model identifies true positives correctly from all the actual positive samples in a dataset.

4. Model Stability and Consistency

Weighted balanced accuracy – This is an accuracy metric that adjusts accuracy based on class weights, where classes with lower weights receive higher weightage.

5. Decision Threshold Consideration

Precision-Recall Curve (AUC-PR) – This is a precision metric used as a measure of prediction efficacy when there are imbalanced classes involved. Using the Precision-Recall Curve for decision threshold values, select the best value such that it has high precision or high recall.

Iterative Improvement Beyond the Baseline 

Without iterative improvement, your baseline model will not yield the desired results. Your analytical models and predictive modeling need iteration to come to their full potential. Hence, it is important to go beyond the initial baseline, continuously refine, and enhance results to stay ahead of your competition.

1. Analyze Your Baseline’s Performance Metrics

Iterative improvement begins with a deep analysis of your baseline model's performance metrics. Evaluate your baseline’s accuracy, precision, recall, and other relevant metrics to identify what’s working and what requires improvement.

Leverage advanced optimization techniques, like hyperparameter tuning and feature engineering, to make targeted adjustments that further enhance the model's predictive capabilities.

2. Incorporate Feedback Loops

Feedback loops in the development process let you learn and adapt in real-time. It helps fine-tune existing models and also accommodates evolving user needs and data patterns.

ML feedback loops are mostly driven by data drifts. It requires you to analyze, monitor, and retrain models over time to keep them updated. Experiment with different algorithms, data augmentation, and training methods to ensure achieving the optimal outcome.

5 Main Challenges and Considerations 

When building a baseline model, there are a few specific challenges you must look into and address them right away. Here are the top 5.

1. Data Quality and Completeness

Incomplete, redundant, or inaccurate datasets are more likely to show skewed results, leading to a low-performing model. Invest your time and resources in cleaning up your datasets, organizing and validating them, and ensuring that all your data is complete and of high quality to build reliable baseline models.

2. Model Complexity

Baseline models must be simple. Overly complex models may lead to overfitting, meaning the model may perform well on training data but fail to adapt to new datasets. However, building too simple models may not capture complex and intricate data patterns. So, strike a balance between complexity and simplicity and experiment to achieve optimal performance.

3. Feature Selection and Engineering

One of the crucial steps in building baseline models is feature selection. Determining the most relevant features and developing new ones can largely impact the baseline model’s performance.

To overcome this challenge, get a deeper understanding of the data and the domain. Experiment with different feature combinations and extraction methods to enhance the models’ capability to discern meaningful patterns.

4. Imbalanced Datasets

Real-world datasets often have imbalances, where certain classes may be underrepresented. Here, the challenge is that when models are trained on imbalanced data, they may exhibit biased predictions.

Overcoming this challenge requires specialized algorithms, over-sampling or under-sampling, to handle the dataset imbalances and ensure fair and accurate model outcomes.

5. Metric Selection and Evaluation

Without the right evaluation metrics, you cannot determine how well your baseline model is performing. Choose the metrics depending on the nature of the problem—classification, regression, or clustering.

Each of these problems requires you to assess different metrics like accuracy, precision, recall, F1 score, R-squared, or gain and lift charts. Define your business objectives first, know the characteristics of the data, and understand the nature of the problem to choose the right metrics.

Baseline Model in Action: Examples

Here are two real-world examples to help you understand how baseline models help businesses in various ways.

Example 1

Suppose you are a telecommunications company using a logistic regression baseline model for predicting customer churn. In this case, your baseline model might work based on historical data and basic features like monthly charges, customer tenure, and service usage. Here, the model serves as the initial predictive framework for understanding the factors influencing customer churn.

Example 2

Suppose you are an eCommerce company using a simple heuristic baseline model for sentiment analysis in customer reviews. Your baseline model may assume positive sentiments for reviews that have more positive words than negative ones. This can help you assess feedback quickly, categorize positive and negative reviews, and identify areas to improve the overall customer experience.

Wrapping Up

Baseline models are a starting point to help you evaluate your advanced ML models. Not only this, baseline models are the foundation on which your ML projects stand. So, by establishing a robust baseline model and the right evaluation metrics, you can ensure the development of efficient and high-performing models.

Start building your baseline model today using MarkovML’s no-code data-centric AI platform. Sign up for a free trial now!